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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 224-229     DOI: 10.6046/gtzyyg.2018.03.30
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Research on application of unmanned aerial vehicle technology to dynamic monitoring of reserves in the Shouyun iron mine, Beijing
Jie XIANG1,2, Jianping CHEN2(), Shi LI2, Zili LAI2, Haozhong HUANG2, Jing LIU2, Shuai XIE2
1. MNR Key Laboratory of Metallogeny and Mineral Assessment, Institute of Mineral Resources, CAGS, Beijing 100037, China
2. School of Earth Sciences and Resources, China University of Geosciences(Beijing), Beijing 100083, China;
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Abstract  

An open challenge for the remote sensing community is to explore a fast, accurate and low-cost method to monitor the open-pit mining. For this purpose, the authors selected the Shouyun iron mine as the case study. Firstly, the authors implemented field campaigns and data acquisition of unmanned aerial vehicle(UAV) in August 2014 and October 2016. Secondly, the authors generated high-resolution of the digital surface model (DSM) and digital orthophoto map (DOM) by using UAV structure from motion (SfM) technology. Finally, the volumetric changes of reserves were calculated by using the algorithm of DSM of difference (DoD), and then multiplied by the average of ore-bearing rate, density of iron ore and ore grade to obtain the mined tonnage. The result shows that the UAV and SfM technology could be a fast and accurate solution for monitoring the reserves of open-pit mines. This study provides a new idea for dynamic monitoring of reserves and environment in open-pit mine.
Keywords UAV      SfM      open-pit mine’s reserves;      dynamic monitoring      Shouyun iron mine     
:  TP79  
Corresponding Authors: Jianping CHEN     E-mail: 3s@cugb.edu.cn
Issue Date: 10 September 2018
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Jie XIANG
Jianping CHEN
Shi LI
Zili LAI
Haozhong HUANG
Jing LIU
Shuai XIE
Cite this article:   
Jie XIANG,Jianping CHEN,Shi LI, et al. Research on application of unmanned aerial vehicle technology to dynamic monitoring of reserves in the Shouyun iron mine, Beijing[J]. Remote Sensing for Land & Resources, 2018, 30(3): 224-229.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.30     OR     https://www.gtzyyg.com/EN/Y2018/V30/I3/224
Fig.1  Location of Shouyun iron mine in Miyun district, Beijing
型号 重量/g 空间分辨
率/ m		 焦距 /mm 传感器尺
寸/mm		 数据大小/
像素×像素
Sony
QX100		 179 20.9 10.4~37.1 13.2 × 8.8 5 472 × 3 648
Tab.1  Parameters of camera equipped on UAV
Fig.2  Field surveys and data acquisition lines of UAV
Fig.3  Cloud point and DSM of two periods using UAV data
Fig.4  Results of DSM of difference by using the limit of detection method
Fig.5  Results of DSM of difference by using the converted probability method
方法 堆积/m3 开挖/m3 总体积变化/m3 储量变化/t
原始DoD 1 325 479 -14 633 968 -13 308 489 -1 916 422
最小检出限法(0.5 m) 1 264 146 -14 559 008 -13 294 862 -1 914 460
最小检出限法摒弃量 61 333 -74 960 -13 627 -1 962
转换概率法(95%) 1 262 525 -14 556 857 -13 294 332 -1 914 384
转换概率法摒弃量 62 954 -77 111 -14 157 -2 039
Tab.2  Comparison of results by different methods
[1] 李德仁, 李明 . 无人机遥感系统的研究进展与应用前景[J]. 武汉大学学报(信息科学版), 2014,39(5):505-513,540.
doi: 10.13203/j.whugis20140045 url: http://d.wanfangdata.com.cn/Periodical/whchkjdxxb201405001
[1] Li D R, Li M . Research advance and application prospect of unmanned aerial vehicle remote sensing system[J]. Geomatics and Information Science of Wuhan University, 2014,39(5):505-513,540.
[2] 胡勇, 张孝成, 马泽忠 , 等. 无人机遥感影像中农村房屋信息快速提取[J]. 国土资源遥感, 2016,28(3):96-101.doi: 10.6046/gtzyyg.2016.03.16.
doi: 10.6046/gtzyyg.2016.03.16 url: http://d.wanfangdata.com.cn/Periodical/gtzyyg201603016
[2] Hu Y, Zhang X C, Ma Z Z , et al. Rural residential area extraction from UAV remote sensing imagery[J]. Remote Sensing for Land and Resources, 2016,28(3):96-101.doi: 10.6046/gtzyyg.2016.03.16.
[3] 唐敏, 李永树, 李歆 , 等. 无人机影像局部增强方法及其在影像匹配中的应用[J]. 国土资源遥感, 2013,25(4):53-57.doi: 10.6046/gtzyyg.2013.04.09.
doi: 10.6046/gtzyyg.2013.04.09 url: 年度引用
[3] Tang M, Li Y S, Li X , et al. Local enhancement method and its applications to UAV image matching[J]. Remote Sensing for Land and Resources, 2013,25(4):53-57.doi: 10.6046/gtzyyg.2013.04.09.
[4] 尚红英, 陈建平, 李成尊 , 等. RS在矿山动态监测中的应用——以新疆稀有金属矿集区为例[J]. 遥感技术与应用, 2008,23(2):189-194.
doi: 10.11873/j.issn.1004-0323.2008.2.189 url: http://d.wanfangdata.com.cn/Periodical_ygjsyyy200802012.aspx
[4] Shang H Y, Chen J P, Li C Z , et al. Application of remote sensing in the dynamic inspection of the mining:An example in the rare metal ore concentration area of Xinjiang[J]. Remote Sensing Technology and Application, 2008,23(2):189-194.
[5] 杨青山, 范彬彬, 魏显龙 , 等. 无人机摄影测量技术在新疆矿山储量动态监测中的应用[J].测绘通报, 2015(5):91-94.
[5] Yang Q S, Fan B B, Wei X L , et al. Research on the application of unmanned aerial vehicle technology in Xinjiang mineral monitoring[J].Bulletin of Surveying and Mapping, 2015(5):91-94.
[6] 金鼎坚, 支晓栋, 王建超 , 等. 面向地质灾害调查的无人机遥感影像处理软件比较[J]. 国土资源遥感, 2016,28(1):183-189.doi: 10.6046/gtzyyg.2016.01.27.
doi: 10.6046/gtzyyg.2016.01.27
[6] Jin D J, Zhi X D, Wang J C , et al. Comparison of UAV remote sensing imagery processing software for geological disasters monitoring[J]. Remote Sensing for Land and Resources, 2016,28(1):183-189.doi: 10.6046/gtzyyg.2016.01.27.
[7] Chen J P, Li K, Chang K J , et al. Open-pit mining geomorphic feature characterisation[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,42:76-86.
doi: 10.1016/j.jag.2015.05.001 url: http://linkinghub.elsevier.com/retrieve/pii/S0303243415001105
[8] Lane S N, Richard K S, Chandler J H . Developments in monitoring and modelling small-scale river bed topography[J]. Earth Surface Processes and Landforms, 1994,19(4):349-368.
doi: 10.1002/esp.3290190406 url: http://doi.wiley.com/10.1002/%28ISSN%291096-9837
[9] Wheaton J M, Brasington J, Darby S E , et al. Accounting for uncertainty in DEMs from repeat topographic surveys:Improved sediment budgets[J]. Earth Surface Processes and Landforms, 2010,35(2):136-156.
doi: 10.1002/esp.1886 url: http://www.cabdirect.org/abstracts/20103127886.html
[10] Brasington J, Langham J, Rumsby B . Methodological sensitivity of morphometric estimates of coarse fluvial sediment transport[J]. Geomorphology, 2003,53(3/4):299-316.
doi: 10.1016/S0169-555X(02)00320-3 url: http://linkinghub.elsevier.com/retrieve/pii/S0169555X02003203
[11] Lane S N, Westaway R M, Hicks D M . Estimation of erosion and deposition volumes in a large,gravel-bed, braided river using synoptic remote sensing[J]. Earth Surface Processes and Landforms, 2003,28(3):249-271.
doi: 10.1002/(ISSN)1096-9837 url: http://doi.wiley.com/10.1002/%28ISSN%291096-9837
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